1. Field of the Invention
The invention relates to the development of computer-assisted diagnostic (CAD) methods for the analysis of digital x-ray images or gray-scale images generated by other digital sensors. More particularly, the invention relates to the use of CAD methods for the analysis of chest x-rays for the detection of lung nodules.
2. Description of Related Art
The use of computer-assisted diagnostic (CAD) methods has been proposed as a second opinion strategy for various medical imaging applications that include breast screening using digital mammography and lung nodule detection. The goals of the CAD methods are to improve sensitivity by increasing the detection of potentially significant suspicious areas and to improve specificity by reducing false-positive interpretations.
Lung nodule detection using conventional planar chest x-ray film typically does not exceed a detection rate of 70%. Typically x-ray CT is used as a follow-up diagnostic procedure. Although x-ray CT has a greater sensitivity of detection, the detection and classification of all LNs within the 3 D volume (stacked 2 D slices) poses a significant logistical and time-consuming research problem (Giger et al., 1994; Buckley et al., 1995). CAD methods are therefore important for both x-ray chest imaging and x-ray CT. However, until now, CAD methods have been found to result in a high rate of false positives (J. S. Lin et al., 1995). The problems associated with CAD methods for lung nodule detection can be attributed to several factors, including that:
1. Lung nodules typically have small structures having very low image contrast and that are spatially distributed in a nonuniform tissue background and image noise; PA1 2. Lung nodules are often adjacent to or hidden by camouflaging structures such as the rib cage, media sternum, or blood vessels, the ribs and vessels being oriented so that they may be viewed in both lateral and cross-section configuration; and PA1 3. Lung nodule features such as circularity, irregularity, and compactness are often similar to blood vessels viewed end on. PA1 1. Segmentation of suspicious areas; and PA1 2. Differentiation of nodules from lung and chest structures (S. B. Lo et al., 1995). PA1 1. CAD modules that are not automatic (operator dependent) and use fixed parameters that are often image dependent as opposed to methods that adapt, for example, to the image noise; PA1 2. Single-scale filtering methods are used for image enhancement or segmentation that do not optimally differentiate LNs from other clinical features with similar pixel intensity characteristics and do not preserve image details, as opposed to multiresolution or multiorientation wavelet transform methods; and PA1 3. Feature extraction is not optimally performed in all domains (grey level, morphological, texture). PA1 1. Unsharp-masking techniques and gradient generation for edge detection using the Sobel or Roberts operator (Tahoces et al., 1991; Daponte et al., 1988); PA1 2. 2 D surface fitting to correct for nonuniform background (Katsuragawa et al., 1988); PA1 3. Morphological operations to selectively enhance circularity of the LNs (Giger et al., 1990 a); and PA1 4. Generalized unsharp-mashing techniques by generating difference images of an enhanced image (2 D profile matching or Fourier transform methods) and a smoothed image (linear or median filters) (Giger et al., 1990 b; Lo et al., 1993). PA1 1. Image noise suppression using a multistage nonlinear tree-structured filter (TSF) using fixed parameters and/or an adaptive multistage nonlinear filter (AMNF); PA1 2. Selective image enhancement or segmentation of clinical features using, for example, multiresolution (M-channel) tree-structured wavelet transforms (TSWTs), which permit improved differentiation of normal anatomical structures from suspicious lung nodule (LN) areas; PA1 3. Multiorientation (N) directional wavelet transforms (DWTs) for additional directional feature enhancement or extraction of specific structures such as the rib cage or blood vessels; PA1 4. A 1.5-dimensional (1.5 D) filter for additional enhancement and segmentation of suspicious lung nodule areas; and PA1 5. Neural networks (NNs) or fuzzy binary decision trees (FBDTs) for classification using features computed in the pixel intensity, morphological, directional texture, and connectivity domains. PA1 1. Fully operator-independent operation to reduce interobserver variation; PA1 2. The preservation of image details for each CAD module using the unique properties of the wavelet transforms; and PA1 3. Improved feature extraction in the grey level, morphological (shape), directional texture, and connectivity domains.
Various CAD lung nodule detection schemes have been realized with two successive steps:
The former can be considered region-of-interest (ROI) localization of suspicious areas, and the latter as a typical pattern recognition problem. Several algorithms proposed in the literature, such as thresholding, 2 D profile matching, and morphological operations, use intensity characteristics on a 2 D matrix and are sensitive to overlapping lung structures, which often appear as stronger signals than nodules in the image (S. B. Lo et al., 1993; M. L. Giger et al., 1990 b). Two-dimensional linear filtering also suffers from high computation cost and a lack of multiscale analysis. A general schematic diagram for the current method of detecting lung nodules is presented in FIG. 1.
Previous methods incorporating CAD modules (Lin et al., 1996; Giger et al., 1994) have limitations that degrade the sensitivity and specificity of LN detection, including:
Previously disclosed image-enhancement methods have included:
Image segmentation methods include global thresholding, adaptive or local thresholding techniques based on pixel intensity histograms and trained classifiers to identify lung fields or asymmetry in chest images (Duryea et al., 1995; McNitt-Gray et al., 1994; Armato et al., 1994).
Feature extraction has proved to be a very difficult task in order to differentiate LNs from end-on vessels or recognize LNs located near rib cage crossings. Methods include region growing to generate various grey-level and morphological features (Matsumoto et al., 1992; Wu et al., 1994). Classification methods used have included discriminant analysis, rule-based schemes, and trained neural networks (Wu et al., 1994; Lo et al., 1993, 1995; Lin et al., 1996). These classification methods aim at reducing the false positive rate in the suspicious area segmentation, the sensitivity of which has been on the order of 70% with a large false positives (FPs) to image ranging from 4 to 12. It is reported that these methods can reduce the false positive rate. Because of the differences and limitation of the databases used, it is difficult to evaluate and compare quantitatively these CAD methods.
Wavelet theory has recently been developed as a unifying framework. Multiresolution signal processing, which is used in computer vision, subband coding developed for image/speech compression, and wavelet series expansion as developed in applied mathematics, has recently been recognized as a different aspect of signal processing theory (Rioul et al., 1991). The application of wavelet theory in mammogram processing includes feature enhancement (Laine et al., 1995), detection of microcalcifications (Qian et al., 1995 b), differentiation of mass from normal tissue (Karssemeijer, 1994). For example, Laine et al. (1995) used linear, exponential, and weighted functions successfully for the modification of the coefficients of dyadic wavelet transform for improving the local feature visibility. This work is focused on image enhancement for improved visual diagnosis, as opposed to improving feature extraction.